GDHQNHL15_f-Advanced Metrics 2

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still trying to find its role.
“In baseball, there is
a debate over the value
of certain things within
proven metrics,” Dubas
says. “In hockey, we’re
trying to figure out what
the metrics are.”
Dubas is perfect to
represent the vanguard
in this area. A 28-yearold
whom some might
consider to look more
like a player than an
executive, he was hired
by Toronto over the
summer as an assistant
GM and has been
charged with helping the
Maple Leafs integrate
traditional methods
KYLE DUBAS
of scouting and accumulating players with more modern methods of
statistical assessment. Since he admits that NHL teams are trying to
navigate the many different possible courses of action available, his job
isn’t particularly clear yet.
Because of that, this is an alternately exciting and confusing time
for hockey, because teams have to figure out the best ways to approach
a new universe, the better to understand how to make numbers work
for them, without torpedoing methodologies that have made them
successful to this point. A good indicator of how much clubs are grappling
with this is that 11 of the 12 teams contacted for comment on this topic
refused to talk, in part because they don’t want to reveal proprietary
information, but also because they are still figuring out how to make
it work for them.
“I don’t think anything guarantees more wins,” Dubas says. “I
think you can guarantee a better process, if you are willing to look at
deeper analysis.
“In baseball, you can drill into your experience to see what works.
Hockey isn’t at that point yet.”
Despite Dubas’ age and youthful (really youthful) look, he is perfectly
positioned to help the Maple Leafs stand at the forefront of the NHL’s
analytics movement—and not just because he knows how to work an
iPhone better than other front office types in the league. Dubas comes
from a hockey family, and his grandfather, Walter, coached the Sault
Ste. Marie Greyhounds, a Northern Ontario junior club, during the ’60s.
Dubas says he has “been working every day in hockey since I was 11
years old.” His playing career ended when he was 14, due to concussions,
but he leapt immediately into the executive world of the sport, helping
the Greyhounds in hockey operations and on the business side.
He graduated at age 20 from Brock University in St. Catharines with
a degree in sport management and a minor in Economics and by 22 was
the youngest player agent certified by the NHL Players Association.
Three years later, he was the GM of the Greyhounds and began to
blend his eye for talent—based on a growing resume of experience—
with some metrics.
“When you evaluate players, you evaluate them according to
traditional scouting methods,” Dubas says. “When you invest yourself in
an analytical approach to sports, it helps you to open your mind, and you
can incorporate statistics and analytics into scouting.”
In baseball, a battle raged for years between the guys with advanced
degrees in math and science and the old guard scouts. Some might say it’s
still going on, as the analytics side ridicule gut instincts and “a good eye
Graig Abel/NHL/Getty Images
Meet Corsi & Fenwick
While advanced metrics in hockey might be difficult to get used to, it’s all based on traditional stats, just combined to come up with
a neat number that tries to sum up a player’s effectiveness while on the ice. Named after the men who came up with the metric, Corsi
and Fenwick is not as intimidating as it seems. They’re both stats used to measure a player’s ability to possess the puck (think time
of possession in football) since having the puck correlates to scoring (you have to have the puck in order to score).
Corsi = shots on goal + missed shots + blocked shots
Fenwick = shots on goal + missed shots
They’re very similar with the exclusion of blocked shots with Fenwick.
To use both Corsi and Fenwick, they are both broken into “For” and “Against.” “For” is a positive event (a shot on goal) for his team;
“Against” is when it happens to his team (an opponent’s shot on goal) while he is on the ice. It’s like plus/minus as the net score is
the player’s Fenwick/Corsi score.
Example:
Player A’s Fenwick For (FF) = 24 and his Fenwick Against (FA) = 6. His night would net as a +18 Fenwick.
Player A’s Corsi For (CF) = 30 and his Corsi Against (CA) = 10. He would have a +20 Corsi
Furthering the Corsi/Fenwick number is breaking it into a percentage, which can then be better used to compare against other
players. Both Corsi For Percentage (CF%) and Fenwick For Percentage (FF%) is used to figure out his effectiveness during the game
with or without the puck. The formula for CF% is CF divided by (CF + CA).
Using the same example above, Player A’s night would be CF% of 80.0 and a FF% of 75.0.
Whether it be a percentage or a plus/minus number, the Corsi/Fenwick metric is just a way to sum up a player’s worth while on
the ice. While it’s not an all-encompassing stat to determine a player’s ability, it does show, especially looking at the wide view of
several seasons, a trend in what positives he can bring while on ice.